Super-spreaders of novel coronaviruses that cause SARS, MERS and COVID-19: a systematic review.

PURPOSE: Most index cases with novel coronavirus infections transmit disease to just one or two other individuals, but some individuals "super-spread"-they infect many secondary cases. Understanding common factors that super-spreaders may share could inform outbreak models, and be used to guide contact tracing during outbreaks. METHODS: We searched in MEDLINE, Scopus, and preprints to identify studies about people documented as transmitting pathogens that cause SARS, MERS, or COVID-19 to at least nine other people. We extracted data to describe them by age, sex, location, occupation, activities, symptom severity, any underlying conditions, disease outcome and undertook quality assessment for outbreaks published by June 2021. RESULTS: The most typical super-spreader was a male age 40+. Most SARS or MERS super-spreaders were very symptomatic, the super-spreading occurred in hospital settings and frequently the individual died. In contrast, COVID-19 super-spreaders often had very mild disease and most COVID-19 super-spreading happened in community settings. CONCLUSIONS: SARS and MERS super-spreaders were often symptomatic, middle- or older-age adults who had a high mortality rate. In contrast, COVID-19 super-spreaders tended to have mild disease and were any adult age. More outbreak reports should be published with anonymized but useful demographic information to improve understanding of super-spreading, super-spreaders, and the settings in which super-spreading happens.

English care home staff morale and preparedness during the COVID pandemic: A longitudinal analysis.

BACKGROUND: Staff actions to prevent infection introduction and transmission in long-term care facilities (LTCFs) were key to reducing morbidity and mortality from COVID-19. Implementing infection control measures (ICMs) requires training, adherence and complex decision making while trying to deliver high quality care. We surveyed LTCF staff in England about their preparedness and morale at 3 timepoints during the COVID-19 epidemic. METHODS: Online structured survey targeted at LTCF workers (any role) administered at 3 timepoints (November 2020-January 2021; August-November 2021; March-May 2022). Narrative summary of answers, narrative and statistical summary (proportionality with Pearson's chi-square or Fisher's Exact Test) of possible differences in answers between waves. RESULTS: Across all 3 survey waves, 387 responses were received. Morale, attitudes towards working environment and perception about colleague collaboration were mostly positive at all survey points. Infection control training was perceived as adequate. Staff felt mostly positive emotions at work. The working environment remained challenging. Masks were the single form of PPE most consistently used; eye protection the least used. Mask-wearing was linked to poorer communication and resident discomfort as well as mild negative health impacts on many staff, such as dehydration and adverse skin reactions. Hand sanitizer caused skin irritation. CONCUSIONS: Staff morale and working practices were generally good even though the working environment provided many new challenges that did not exist pre-pandemic.

Enhancing epidemiological surveillance of the emergence of the SARS-CoV-2 Omicron variant using spike gene target failure data, England, 15 November to 31 December 2021.

When SARS-CoV-2 Omicron emerged in 2021, S gene target failure enabled differentiation between Omicron and the dominant Delta variant. In England, where S gene target surveillance (SGTS) was already established, this led to rapid identification (within ca 3 days of sample collection) of possible Omicron cases, alongside real-time surveillance and modelling of Omicron growth. SGTS was key to public health action (including case identification and incident management), and we share applied insights on how and when to use SGTS.

The spatio-temporal distribution of COVID-19 infection in England between January and June 2020.

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

Community use of face masks and similar barriers to prevent respiratory illness such as COVID-19: a rapid scoping review.

BackgroundEvidence for face-mask wearing in the community to protect against respiratory disease is unclear.AimTo assess effectiveness of wearing face masks in the community to prevent respiratory disease, and recommend improvements to this evidence base.MethodsWe systematically searched Scopus, Embase and MEDLINE for studies evaluating respiratory disease incidence after face-mask wearing (or not). Narrative synthesis and random-effects meta-analysis of attack rates for primary and secondary prevention were performed, subgrouped by design, setting, face barrier type, and who wore the mask. Preferred outcome was influenza-like illness. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) quality assessment was undertaken and evidence base deficits described.Results33 studies (12 randomised control trials (RCTs)) were included. Mask wearing reduced primary infection by 6% (odds ratio (OR): 0.94; 95% CI: 0.75-1.19 for RCTs) to 61% (OR: 0.85; 95% CI: 0.32-2.27; OR: 0.39; 95% CI: 0.18-0.84 and OR: 0.61; 95% CI: 0.45-0.85 for cohort, case-control and cross-sectional studies respectively). RCTs suggested lowest secondary attack rates when both well and ill household members wore masks (OR: 0.81; 95% CI: 0.48-1.37). While RCTs might underestimate effects due to poor compliance and controls wearing masks, observational studies likely overestimate effects, as mask wearing might be associated with other risk-averse behaviours. GRADE was low or very low quality.ConclusionWearing face masks may reduce primary respiratory infection risk, probably by 6-15%. It is important to balance evidence from RCTs and observational studies when their conclusions widely differ and both are at risk of significant bias. COVID-19-specific studies are required.